A genetic algorithm architecture implements a two-stage completion genetic algorithm with respect to an evolving current population data set. The two-stage completion genetic algorithm that includes genotype and phenotype completion loops. The genotype completion loop operates to compete the current population data set based on genotype field fitness scores. The genotype completion loop also implements a phenogenesis operator used to generate a current phenotype set. The phenotype completion loop operates, concurrently with the genotype completion loop, to evaluate the current phenotype set, constrained relative to the current population data set, against a fitness function to produce phenotype fitness scores. The phenotype completion loop implements a genotype reduction operator that then determines corresponding genotype fitness scores for use as the basis for competition in the genotype completion loop.

A computer implemented adaptive ensemble classifier is provided which includes: a plurality of classifiers; a decision structure that maps respective classifier combinations to respective classification decision results; and a plurality of respective sets of weights associated with respective classifier combinations.

Disclosed are methods, systems, and processor program products that include executing an optimization scheme to obtain a first solution set, presenting the first solution set to at least two users, receiving rankings of the first solution set from the at least two users, aggregating the rankings, and, generating a second solution set based on the aggregated rankings. The optimization scheme can include a genetic algorithm. In embodiments, at least a part of the first solution set can be presented to the users based on the parts of the solution set associated with the user (e.g., user's knowledge).

Disclosed are methods, systems, and/or processor program products that include generating a population of genotypes, the genotypes based on at least one stimulus to a system, measuring at least one response of the system upon providing the population of genotypes to at least one model of the system, and, based on the measured at least one response of the system, performing at least one of: (a) applying at least one genetic operator to at least some of the population of genotypes, and iteratively returning to generating a population of genotypes, and (b) associating a condition of the system with at least one of the population of genotypes.

A method, computer program storage medium and system that implement evolutionary algorithms on heterogeneous computers; in which a central process resident in a central computer delegates subpopulations of individuals of similar fitness from a central pool to separate processes resident on peripheral computers where they evolve for a certain number of generations after which they return to the central pool before the delegation is repeated.